Cognitive Science 0342 - Statistical Learning

The rise of computers and large datasets over the past 30 years has led to the development of new methods for analyzing data. These 'statistical learning' methods blend classical statistical concepts with ideas from computer science and are widely used by data scientists to analyze complex datasets. In this class we will cover the basic concepts in statistical learning including: regression, supervised learning (classification), unsupervised learning (clustering and dimensionality reduction), cross-validation methods, and model selection. We will use the R programming language to explore the usefulness of different methods and to analyze real data. The class work will consist of weekly programming problems and a final project. Prerequisites: Prior experience with programming and statistics, either through a class or from other experiences.

Quantitative Skills In this course, students are expected to spend at least six to eight hours a week of preparation and work outside of class time. This time includes reading, writing, and research.